Annales Geophysicae
Annales Geophysicae
ANGEO
Articles | Volume 42, issue 1
Articles | Volume 42, issue 1
https://doi.org/10.5194/angeo-42-103-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/angeo-42-103-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 42, issue 1
Regular paper
 | 
25 Apr 2024
Regular paper |  | 25 Apr 2024

Auroral breakup detection in all-sky images by unsupervised learning

Noora Partamies, Bas Dol, Vincent Teissier, Liisa Juusola, Mikko Syrjäsuo, and Hjalmar Mulders

Viewed

Total article views: 529 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
383 105 41 529 25 25
  • HTML: 383
  • PDF: 105
  • XML: 41
  • Total: 529
  • BibTeX: 25
  • EndNote: 25
Views and downloads (calculated since 30 Nov 2023)
Cumulative views and downloads (calculated since 30 Nov 2023)

Viewed (geographical distribution)

Total article views: 529 (including HTML, PDF, and XML) Thereof 572 with geography defined and -43 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Jun 2024
Download
Short summary
Auroral imaging produces large amounts of image data that can no longer be analyzed by visual inspection. Thus, every step towards automatic analysis tools is crucial. Previously supervised learning methods have been used in auroral physics, with a human expert providing ground truth. However, this ground truth is debatable. We present an unsupervised learning method, which shows promising results in detecting auroral breakups in the all-sky image data.
Read more